A large enterprise has many data sources and a complex data landscape. They need to ensure that data analysts can trust the data they use. They want to implement a solution that provides a single view of all data assets, including their origin, transformations, and quality. What is the most comprehensive approach?

Implement a data lake and store all raw data in one place.

Use data profiling tools to periodically assess data quality and report issues.

Implement a data catalog that captures technical and business metadata, lineage, and data quality scores.

Create a data warehouse that integrates all data into a single schema.

Which of the following is a typical output of data profiling?

A summary of data types, null values, and value distributions

A backup of the data to another region

A list of all data access permissions

What is the primary purpose of a data catalog?

To store the actual data files for analysis

To document data assets, metadata, and lineage

To enforce data quality rules on all data

To provide a backup and recovery mechanism for data

During the design phase for a new data processing system, which activity is an example of requirements mapping?

Running a load test on a prototype to measure performance

Deploying a standard reference architecture without modification

Interviewing business stakeholders to capture growth projections and compliance needs, then selecting components that meet those needs

Choosing a data processing framework based on the team's familiarity

An e-commerce company receives order data from multiple sources. They want to load data into BigQuery for analysis, but they need to clean and transform the data before loading. They also want to be able to reprocess data if errors are found. What is the best way to stage the data?

Use Cloud SQL as a staging database before loading into BigQuery.

Load data directly into BigQuery and use SQL to clean it.

Use Cloud Functions to transform data on the fly and write to BigQuery.

Land raw data in Cloud Storage, then use Dataflow to transform and load into BigQuery.

Google CloudProfessional Data Engineer

Domain 1Objective 3

1.3 Designing for Flexibility and Portability PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 5)

Part of the Designing data processing systems domain, which accounts for ~22% of the PROFESSIONAL-DATA-ENGINEER exam. Google Cloud does not publish an official question count, but from its 120-minute exam (~50–80 total, ~11–18 in this domain), expect 3–5 from this objective — we provide 30 practice questions to prepare you well beyond it. (estimate)

30questions here

6free pages

8concepts

~22%of the exam

Questions 21–25

21expert · hard
A large enterprise has many data sources and a complex data landscape. They need to ensure that data analysts can trust the data they use. They want to implement a solution that provides a single view of all data assets, including their origin, transformations, and quality. What is the most comprehensive approach?
Select an answer first
22foundation · easy
Which of the following is a typical output of data profiling?
Select an answer first
23foundation · easy
What is the primary purpose of a data catalog?
Select an answer first
24foundation · easy
During the design phase for a new data processing system, which activity is an example of requirements mapping?
Select an answer first
25application · medium
An e-commerce company receives order data from multiple sources. They want to load data into BigQuery for analysis, but they need to clean and transform the data before loading. They also want to be able to reprocess data if errors are found. What is the best way to stage the data?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to PROFESSIONAL-DATA-ENGINEER

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by Google Cloud. “PROFESSIONAL-DATA-ENGINEER” is a trademark of its owner, used for identification only.